Is the cloud the key to democratizing AI? | Tech News

At the peak of the Japanese harvest, Makoto Koike’s mother spends around eight hours a day sorting cucumbers from the family farm into different categories—a dull, time-consuming task that her son decided to automate. Although Makoto wasn’t a machine learning expert, he started playing around with TensorFlow, Google’s popular open-source machine learning framework, and developed a deep learning model that could sort cucumbers by size, shape and other attributes. The system isn’t perfect (it has an accuracy rate of around 75 percent). But it’s a sign of how AI could soon transform even the smallest family-run business.

Giants like Google, Amazon, Microsoft, Apple, and Facebook are, of course, well-aware of this transformative power. Deep learning underpins Amazon’s recommendation system, Google’s search and translation tools, and Microsoft’s Cortana personal assistant, as well many other widely used applications and services. Most Fortune 500 companies also have dedicated AI teams in place. But the big beasts’ interest in AI has drained the pool of data scientists, which leaves most smaller and medium enterprises in the same boat as Makoto: eager to explore how AI can improve their business, but short on expertise.

Even those enterprises that can afford to hire top AI experts still need to prepare huge data sets, and spend considerable sums on computing power to analyze them and teach their neural network to recognize certain patterns or objects. However, the big cloud providers are conscious of these issues—and they believe they’ve found a way to help people overcome them.

Machine learning as a service, or cloud AI, is now a major component of cloud platforms like Amazon Web Services (AWS), Microsoft Azure, Google Cloud, and IBM Cloud. Essentially these companies are offering to do the grunt work involved in adding AI to business applications by providing their customers with access to pretrained deep learning models—for image recognition, say—as well as tools that simplify the process of building, training and deploying customized models on the cloud.

“There are tools for data scientists who know how to code; there are tools for software developers that may not know how to properly tune algorithms, but who can build apps if you give them an API to code against; and finally there are tools for clickers, who basically relate through GUIs, which covers the vast majority of people in the world,” says Chris Nicholson, CEO of Skymind, a provider of deep learning tools for the enterprise.